SciML/SciMLSensitivity.jl

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

What it solves

It provides utilities for sensitivity analysis within the Scientific Machine Learning (SciML) ecosystem, allowing users to understand how changes in input parameters affect the output of scientific models.

How it works

It functions as a component package that provides the underlying sensitivity analysis tools used by other libraries in the SciML ecosystem, such as DifferentialEquations.jl.

Who it’s for

Researchers and developers working with scientific machine learning who need to perform sensitivity analysis on their models.

Highlights

  • Part of the broader SciML Scientific Machine Learning ecosystem
  • Integrated with DifferentialEquations.jl

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